Development of an AI-Assisted Pest Prediction and Early Warning System for Protected Horticultural Environments

Abstract

The application of hydroponic farming as a solution to sustainable agriculture has proven to have a high degree of efficiency in terms of water use, controlled growth conditions and high crop yield. Nevertheless, the optimal balance of nutrient remains highly an important challenge since the nutrient requirements are in a continuous change based on the stage of plant growth as well as the environment. This paper presents a Smart Hydroponic Nutrient Balancing System with the help of Reinforcement Learning (RL) in order to produce lettuce in a sustainable manner. The suggested system combines the Internet of Things (IoT)-based environmental monitoring, automated nutrition control, and smart decision-making into a single smart hydroponic system. Embedded sensors and wireless communication modules are used to constantly measure real-time parameters such as pH, electrical conductivity (EC), temperature, humidity, dissolved oxygen and nutrient concentration. A reinforcement learning agent adaptively controls the nutrient dosage, circulation of water, and environmental adaptations to achieve optimal growth of lettuce and reduce wastage of nutrients, water, and energy. A hydroponic testbed with Nutrient Film Technique (NFT)-based facility was used to perform experimental evaluation through the use of the 96 lettuce plants in a 45-day growth cycle. Findings showed that there was a 24.8 percent increase in yield of lettuce and a 37.6 percent decrease in water use compared to conventional rule-based system. Significant resource efficiency and nutrient stability improvements were statistically validated. The suggested system has a high potential of smart precision horticulture, eco-friendly hydroponic farm and AI horticulture apps.